A Comparison of Machine Learning Methods for the Prediction of Traffic Speed in Urban Places

Rising interest in the field of Intelligent Transportation Systems combined with the increased availability of collected data allows the study of different methods for prevention of traffic congestion in cities. A common need in all of these methods is the use of traffic predictions for supporting p...

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Bibliographic Details
Published in:Sustainability 2020-01, Vol.12 (1), p.142
Main Authors: Bratsas, Charalampos, Koupidis, Kleanthis, Salanova, Josep-Maria, Giannakopoulos, Konstantinos, Kaloudis, Aristeidis, Aifadopoulou, Georgia
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Decision trees
Intelligent transportation systems
Learning algorithms
Machine learning
Multilayer perceptrons
Neural networks
Regression analysis
Roads
Roads & highways
Support vector machines
Sustainability
Traffic congestion
Traffic flow
Traffic management
Traffic planning
Traffic signals
Traffic speed
Transportation networks
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Summary:Rising interest in the field of Intelligent Transportation Systems combined with the increased availability of collected data allows the study of different methods for prevention of traffic congestion in cities. A common need in all of these methods is the use of traffic predictions for supporting planning and operation of the traffic lights and traffic management schemes. This paper focuses on comparing the forecasting effectiveness of three machine learning models, namely Random Forests, Support Vector Regression, and Multilayer Perceptron—in addition to Multiple Linear Regression—using probe data collected from the road network of Thessaloniki, Greece. The comparison was conducted with multiple tests clustered in three types of scenarios. The first scenario tests the algorithms on specific randomly selected dates on different randomly selected roads. The second scenario tests the algorithms on randomly selected roads over eight consecutive 15 min intervals; the third scenario tests the algorithms on random roads for the duration of a whole day. The experimental results show that while the Support Vector Regression model performs best at stable conditions with minor variations, the Multilayer Perceptron model adapts better to circumstances with greater variations, in addition to having the most near-zero errors.
ISSN:2071-1050
2071-1050
DOI:10.3390/su12010142